Causal Survival Forests with Negative Controls

2026-08-20

AI Summary

本文通过结合因果生存森林与负控制方法，提出了一种新的非参数异质性治疗效应估计方法NC-CSF，以解决观察性生存研究中因删失结果和未测量混杂因素引起的问题。

Abstract

We study heterogeneous treatment-effect (HTE) estimation in observational survival studies commonly associated with both censored outcomes and unmeasured confounding. We integrate causal survival forests (CSF) with negative controls (NC) from proximal causal inference and introduce Negative Control Causal Survival Forests (NC-CSF), a flexible nonparametric HTE learner for survival analysis. Our approach uses a loss that incorporates proxy variables and Neyman orthogonalization to train the random forest, thereby mitigating bias from unobserved confounding and gaining robustness to nuisance estimation. Through extensive simulations spanning varying levels of confounding, proxy relevance, and censoring mechanisms, we demonstrate that NC-CSF substantially reduces bias and estimation error relative to existing baselines. We further demonstrate the practical utility of our method on various clinical datasets, where it confirms several existing findings and also reveals new interpretable patterns of treatment-effect heterogeneity. To facilitate practical use, we provide an end-to-end Python implementation of NC-CSF that carefully handles implementation details such as nuisance estimation and clipping.

Problem

• heterogeneous treatment-effect
• observational survival studies
• unmeasured confounding
• censored outcomes

Innovation

• Negative Control Causal Survival Forests (NC-CSF)
• Neyman orthogonalization
• unobserved confounding